Estimation of gestational age from fundal height: a solution for resource-poor settings

Many women in resource-poor settings lack access to reliable gestational age assessment because they do not know their last menstrual period; there is no ultrasound (US) and methods of newborn gestational age dating are not practised by birth attendants. A bespoke multiple-measures model was developed to predict the expected date of delivery determined by US. The results are compared with both a linear and a nonlinear model. Prospectively collected early US and serial symphysis-pubis fundal height (SFH) data were used in the models. The data were collected from Karen and Burmese women attending antenatal care on the Thai–Burmese border. The multiple-measures model performed best, resulting in a range of accuracy depending on the number of SFH measures recorded per mother (for example six SFH measurements resulted in a prediction accuracy of ±2 weeks). SFH remains the proxy for gestational age in much of the resource-poor world. While more accurate measures should be encouraged, we demonstrate that a formula that incorporates at least three SFH measures from an individual mother and the slopes between them provide a significant increase in the accuracy of prediction compared with the linear and nonlinear formulae also using multiple SFH measures.     

An innovation funnel process for set-based conceptual design via DOE exploration,DEA selection and computer simulation

This paper presents a conceptual design approach including pattern creation from designers, alternative exploration with a DOE matrix, alternative analysis via computer simulation and alternative selection by DEA analysis. Designers possessing domain knowledge create various design patterns to meet the requirements of product performance and customer expectations. Then, based on these design patterns, the alternatives, considered as decision-making units (DMUs), are extracted from various quality level combinations by following the use of the DOE matrix. The nature of the DOE matrix ensures that distinctive representatives are constructed for all design alternatives. The total alternatives (DMUs) consist of the alternatives associated with all the patterns. Computer simulation with ANSYS software is introduced to convert the quality level combination of each alternative (DMU) into simulated outputs, which are further categorised into DEA inputs and DEA outputs for DEA frontier analysis. Four DEA methods, CCR-min input, CCR-max output, BCC-min input and BCC-max output, are used for analysing typical market representatives resulting from market uncertainty. The found efficiencies are used to rank and select the explored alternatives (DMUs) for the next stage of the detailed design. A bike-frame product is chosen as an example to demonstrate the proposed approach. The results clearly show that the proposed approach enables designers to economically select appropriate design alternatives that satisfy performance expectations during the conceptual design stage.     

Effect of chemicals on the microbial evolution in foods

In contrast with most chemical hazardous compounds, the concentration of food pathogens changes during processing, storage, and meal preparation, making it difficult to estimate the number of microorganisms or the concentration of their toxins at the moment of ingestion by the consumer. These changes are attributed to microbial proliferation, survival, and/or inactivation and must be considered when exposure to a microbial hazard is assessed. The number of microorganisms can also change as a result of physical removal, mixing of food ingredients, partitioning of a food product, or cross-contamination (M. J. Nauta. 2002. Int. J. Food Microbiol. 73:297-304). Predictive microbiology, i.e., relating these microbial evolutionary patterns to environmental conditions, can therefore be considered a useful tool for microbial risk assessment, especially in the exposure assessment step. During the early development of the field (late 1980s and early 1990s), almost all research was focused on the modeling of microbial growth over time and the influence of temperature on this growth. Later, modeling of the influence of other intrinsic and extrinsic parameters garnered attention. Recently, more attention has been given to modeling of the effects of chemicals on microbial inactivation and survival. This article is an overview of different applied strategies for modeling the effect of chemical compounds on microbial populations. Various approaches for modeling chemical growth inhibition, the growth-no growth interface, and microbial inactivation by chemicals are reviewed.     

Patterned radial GaAs nanopillar solar cells

Photovoltaic devices using GaAs nanopillar radial p-n junctions are demonstrated by means of catalyst-free selective-area metal-organic chemical vapor deposition. Dense, large-area, lithographically defined vertical arrays of nanowires with uniform spacing and dimensions allow for power conversion efficiencies for this material system of 2.54% (AM 1.5 G) and high rectification ratio of 213 (at ±1 V). The absence of metal catalyst contamination results in leakage currents of ～236 nA at -1 V. High-resolution scanning photocurrent microscopy measurements reveal the independent functioning of each nanowire in the array with an individual peak photocurrent of ～1 nA at 544 nm. External quantum efficiency shows that the photocarrier extraction highly depends on the degenerately doped transparent contact oxide. Two different top electrode schemes are adopted and characterized in terms of Hall, sheet resistance, and optical transmittance measurements.     

Direct measurement of mass transfer around a single bubble by micro-PLIFI

In this paper, an original direct and non-intrusive technique using Planar Laser Induced Florescence with Inhibition (PLIFI) is proposed to quantify the local mass transfer around a single spherical bubble rising in a quiescent liquid. The new set-up tracks the mass transferred in the bubble wake for a plane perpendicular to the bubble trajectory instead of a parallel plane as in previous works, thus avoiding optical reflection problems. A spherical bubble is formed in a glass column containing fluorescent dye. A camera with a microscopic lens is placed underneath the column to record cross-sections of the transferred oxygen. A high-speed camera is located far from the column to simultaneously record the bubble position, size, shape and velocity. The dissolved gas inhibits the fluorescence so that oxygen concentration fields can be measured. From this, a calculation method is developed to determine mass transfer on the micro-scale. Experimental results are compared to the Sherwood numbers calculated from the Frössling and Higbie models used for fully contaminated and clean spherical bubbles, respectively. Results show that all experimental Sherwood numbers occur between the two models, which gives credence to the measurements. The new technique is then developed for bubble diameters ranging from 0.7 to 2 mm in six hydrodynamic conditions (1<Re<10², 10²<Sc<10⁶).     

Structure and energetics of hydrogen chemisorbed on a single graphene layer to produce graphane

Chemisorption of hydrogen on graphene is studied using atomistic simulations with the second generation of reactive empirical bond order Brenner inter-atomic potential. The lowest energy adsorption sites and the most important metastable sites are determined. The H concentration is varied from a single H atom, to clusters of H atoms up to full coverage. We found that when two or more H atoms are present, the most stable configurations of H chemisorption on a single graphene layer are ortho hydrogen pairs adsorbed on one side or on both sides of the graphene sheet. The latter has the highest hydrogen binding energy. The next stable configuration is the ortho–para pair combination, and then para hydrogen pairs. The structural changes of graphene caused by chemisorbed hydrogen are discussed and are compared with existing experimental data and other theoretical calculations. The obtained results will be useful for nanoengineering of graphene by hydrogenation and for hydrogen storage.     

Robust processes through latent variable modeling and optimization

A data‐based approach for developing robust processes is presented and illustrated with an application to an industrial membrane manufacturing process. Using historical process data, principal component analysis and partial least squares are used to extract models of the process and of the sensitivities of the process to various disturbances, including raw material variations, environmental conditions, and process equipment differences. Robustness measures are presented to quantify the robustness of the process to each of these disturbances. The process is then made robust (insensitive) to the disturbances over which one has some control (e.g., by modifying the equipment units to which the process is sensitive and imposing specification regions on sensitive raw materials). It is also made robust to disturbances over which one has little control (e.g., environmental variations) by optimizing the process operating conditions with respect to performance and robustness measures. The optimization is easily performed in the low‐dimensional space of the latent variables even though the number of process variables involved is very large. After applying the methodology to historical data from the membrane manufacturing process, results from several months of subsequent operation are used to demonstrate the large improvement achieved in the robustness of the process. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

PFG-NMR self-diffusion in casein dispersions: Effects of probe size and protein aggregate size

The self-diffusion coefficients of different molecular weight PEGs (Polyethylene glycol) and casein particles were measured, using a pulsed-gradient nuclear magnetic resonance technique (PFG-NMR), in native phosphocaseinate (NPC) and sodium caseinate (SC) dispersions where caseins are not structured into micelles. The dependence of the PEG self-diffusion coefficient on the PEG size, casein concentration, the size and the mobility of casein obstacle particles are reported. Wide differences in the PEG diffusion coefficients were found according to the casein particle structure. The greatest reduction in diffusion coefficients was found in sodium caseinate suspensions. Moreover, sodium caseinate aggregates were found to diffuse more slowly than casein micelles for casein concentrations >9 g/100 g H₂O. Experimental PEG and casein diffusion findings were analyzed using two appropriate diffusion models: the Rouse model and the Speedy model, respectively. According to the Speedy model, caseins behave as hard spheres below the close packing limit (10 g/100 g H₂O for SC (Farrer & Lips, 1999) and 15 g/100 g H₂O for NPC (Bouchoux et al., 2009)) and as soft particles above this limit. Our results provided a consistent picture of the effects of diffusant mass, the dynamics of the host material and of the importance of the casein structure in determining the diffusion behavior of probes in these systems.     

Modulation of Mechanical Interactions by Local Piezoelectric Effects

Piezoelectricity is a well‐established property of biological materials, yet its functional role has remained unclear. Here, a mechanical effect of piezoelectric domains resulting from collagen fibril organisation is demonstrated, and its role in tissue function and application to material design is described. Using a combination of scanning probe and nonlinear optical microscopy, a hierarchical structuring of piezoelectric domains in collagen‐rich tissues is observed, and their mechanical effects are explored in silico. Local electrostatic attraction and repulsion due to shear piezoelectricity in these domains modulate fibril interactions from the tens of nanometre (single fibril interactions) to the tens of micron (fibre interactions) level, analogous to modulated friction effects. The manipulation of domain size and organisation thus provides a capacity to tune energy storage, dissipation, stiffness, and damage resistance.